Dense cellular network deployments relying on the use of Massive MIMO technology are becoming very attractive candidates for future radio access technologies. This is partly due to the promise of Massive MIMO for providing very large throughput increases per base station (BS), due to its ability to multiplex a large number of high-rate streams over the same transmission resources. Using Massive MIMO over dense (small cell) deployments translates into massive throughput increases per unit area with respect to existing deployments.
Massive MIMO is also envisioned as a candidate for addressing large variations in user load, including effectively serving user-traffic hotspots. One aspect that such deployments must deal with effectively in this context is the need for load balancing, that is, the need to associate users with cells not only based on relative signal strength to the user from each cell, but also taking into account the relative user-traffic in the vicinity of each cell, with a goal to optimize network-wide performance. Load balancing is even more challenging in emerging dense deployments. First, load balancing becomes even more important with small cells, as these are inherently less planned, thus less regular than macro deployments, with large variability in effective-area coverage. Furthermore, emerging networks are multi-tier networks having tiers with BSs with vast differences in the coverage area. Indeed, load balancing algorithms need to exploit the fact that each user can be served by multiple base stations (BSs), from multiple tiers and possibly over multiple bands, in order to effectively balance the network load across all tiers bands and BS, and while taking into account the fact that BSs from each tier cover different areas and can operate on different bands.
Non-uniform load distribution is considered to be a major challenge in small cell networks. If the load cannot be balanced efficiently, the performance gains that are expected as a result of the increased density of network access points (due to use of small cells) may be distributed in a very non-uniform manner within the user population. Various load-balancing techniques have been proposed for dynamically arranging user load across small cells. These techniques are generally designed considering traditional PHY layer approaches, where one BS serves at most one user at a certain frequency and time resource. But it is well accepted by now that major gains in PHY layer are expected due to MU-MIMO and especially Massive MIMO.
The problem of designing effective load balancing and user association techniques becomes in general more challenging in cases where more than one user is scheduled at the same time and frequency resources, i.e. with multi-user transmission schemes. Indeed, the rate each user receives in the context of a multiuser transmission scheme, such as e.g., Linear Zero-Forcing Beamforming (LZFBF), depends not only on the user's own channel, but also on the number of other users scheduled together with the user for such multiuser transmission as well as the channels of these users. The problem of scheduling user sets to maximize the sum of user rate when LZFBF precoding is applied has been considered, and a greedy algorithm for the user selection when considering a single cell with a single BS has been proposed. The algorithm can be used as a building tool to schedule cellular and cluster MU-MIMO transmissions in cellular networks applying proportional fairness at each BS. These can also be systematically expanded to include a broader range of fairness conditions with a framework of virtual queues.
Scheduling methods are local in that they assume that user to BS association has already taken place, so that the fairness framework can be applied locally at each BS. Predicting a priori the effect that different user-BS associations have on the network-wide fairness provided across the network by these “locally fair” schedulers is, in general, non-trivial. However, when the number of antennas at the BS is much larger than the number of users instantaneously served by a BS in each transmission resource element, the instantaneous user rates “harden” (show much lower variability), and can be accurately predicted by just knowing the size of the serving set at the BS.
MU-MIMO User Scheduling
Although there several methods available in the literature for scheduling multi-user MIMO transmissions at the BS, a widely accepted class of methods involves scheduling policies which, at any given scheduling instant at the BS, schedule the subset of users that would yield the highest expected weighted sum-rate. Each user's expected rate in each scheduled set for transmission is a function on the instantaneous channels of all the users in the scheduled set. Indeed, assuming LZFB transmission as described in the preceding section, at any given resource block the coefficients λk's depend on the instantaneous channel matrix of all users in the scheduling set (served by ZFBF), and in particular, they can be expressed as
                    λ                  k          ,          S                    ⁡              (        t        )              =          1                                                  [                                                                                          H                      →                                                              k                      ,                      S                                        H                                    ⁡                                      (                    t                    )                                                  ⁢                                                                            H                      →                                                              k                      ,                      S                                                        ⁡                                      (                    t                    )                                                              )                                      -              1                                ]                          k          ,          k                      ,where {right arrow over (H)}k,s(t) denote the compound downlink channel matrix for UT-k in the user set Sat the tth resource block. Clearly, since the choice of the user set S and/or resource block (t) affects λk, the expected user rates are a function of both the scheduling set and the instantaneous channel realization. Fixing the scheduling time instance, and assuming LZFBF transmission, the problem of choosing the subset S which maximize the weighted sum-rate is combinatoric in the number of antennas, as the number of possible subsets, S, that can be considered for scheduling grows exponentially fast with the maximum number of users that can be considered for joint scheduling. One solution proposed for this problem relies on a greedy algorithm for user set selection, with at most quadratic complexity.
Another important factor defining the scheduling assignments that are produced by the scheduling policy is the method by use of which the “user weights” are chosen at each scheduling instant prior to performing the weighted sum rate maximization operation. Although many methods exist for choosing these weights, a widely accepted class of methods (because of their ability to result in nearly optimal performance with respect to a fairness criterion belonging to a broad class of fairness criteria) is one that relies on the use of “virtual queues” to determine the instantaneous user-weights in the weight-sum rate optimization.
Load Balancing
Traditionally, association in cellular networks has been user-terminal based. Users measure their signal-level with respect to the beacons of the nearby base stations (BSs) and associate to the base-station with the strongest received signal. A generalization of this principle has been used in heterogeneous networks. In the case of comparing signal strengths from a macro and a small cell, a user-terminal can also apply a “bias” to favor association to the small cell (with respect to the macro cell).
As traffic-load imbalances are far more pronounced in small cells, there has been some recent work in load balancing in small cells. Indeed, small cells are much more sensitive to the cell association policy because of the non-uniformity of cell size, and the smallest average number of users they serve. This non-uniformity can result in extremely imbalanced traffic-load based on a max-SINR cell association. The prior art in this area mainly involves methods of exchanging information between each user and close-by BSs, which attempt to balance their load using signaling exchanges with nearby users. Another related technique, referred to as “cell breathing,” relies on dynamically changing (contracting or expanding) the coverage area depending on the load situation (over-loaded or under-loaded) of the cells by adjusting the cell transmit power. Also note that these works focus on small cells scheduling only single-user transmissions.
The methods described above have important limitations. First, given that the user rates in a MU-MIMO, transmission is not simply a function of large-scale signal-to-interference plus noise ratio (SINR), but in general depend on the scheduling set and the channel realization. Thus, the resulting load-balancing techniques are not extendable in any straightforward resource-efficient manner. Furthermore, the nature of reciprocity-based Massive MIMO TDD makes large scale SINR in a link between a user and all BSs in proximity available given a single uplink pilot broadcast from the user. In this context, a centralized processor can determine the UT-BS associations of the user population among a set of BSs that would serve these users, without involving exchanges with the users. Using such a central controller to both perform load balancing (i.e., to balance the user load across BSs) among the BSs and to schedule transmissions at each of the BSs places computational burden to the central controller.